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Multi-Objective Optimization using Artificial Intelligence Techniques.

By: Contributor(s): Language: English Series: Computational IntelligencePublication details: Switzerland Springer Nature 2020Description: xi, 58 pages; FiguresISBN:
  • 9783030248345
Subject(s): DDC classification:
  • 006.3 M675
Online resources:
Contents:
-1. Introduction to Multi-objective Optimization -2. What is Really Multi-objective Optimization? -3. Multi-objective Particle Swarm Optimization -4. Non-dominated Sorting Genetic Algorithm -5. Multi-objective Grey Wolf Optimizer
Summary: This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Libros Libros CIBESPAM-MFL 006.3 / M675 (Browse shelf(Opens below)) Ej: 1 Available 006065
Libros Libros CIBESPAM-MFL 006.3 / M675 (Browse shelf(Opens below)) Ej: 2 Available 006066

-1. Introduction to Multi-objective Optimization
-2. What is Really Multi-objective Optimization?
-3. Multi-objective Particle Swarm Optimization
-4. Non-dominated Sorting Genetic Algorithm
-5. Multi-objective Grey Wolf Optimizer

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

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